In this section, we show our design choice in the process of studying ConAM. For the ablation studies, we use datasets of CIFAR-10 [1]} and CIFAR-100 [1]}. The CIFAR [1]} dataset is the most used dataset in deep learning. It is divided into CIFAR-10 [1]} with 10 categories and CIFAR-100 [1]} with 100 categories. Each dataset contains 60k RGB images, with 50k used for training and 10k used for testing. Our module design process includes exploring three kinds of attention strategies, exploring the normalization and activation, and verifying on other different models to study the impact of different patch sizes on ConAM.
